System design · AI engineering · Java
Learn system design and AI engineering by building intuition, not memorising.
Deep, practical explanations of how production systems and LLM applications actually work, with diagrams you can step through and labs you can play with. Written by Ravi Kant Shukla, a backend engineer with 9+ years of shipping distributed Java systems.
In-depth posts
Learning paths
AI news brief
Latest
New explainers and deep dives on system design and AI engineering.
Sandboxing Tool-Using AI Agents: Network Egress, DNS, and Least Privilege
Your agent's container says 'no internet', but can it resolve DNS? A practical guide to containing tool-using AI agents: deny-by-default egress, a controlled resolver, scoped credentials, and audit logs.
Designing for High Availability & Disaster Recovery
What the nines actually mean, how to eliminate single points of failure, active-active vs active-passive redundancy, multi-region failover, and setting RPO/RTO targets that match the business, not just the architecture diagram.
Message Queues & Async Processing: Kafka, RabbitMQ, and Event Streaming
Delivery guarantees, Kafka partitions and consumer groups, RabbitMQ exchanges, dead letter queues, and how to decide when async processing is the right call and when it just adds latency.
Learning paths
Pick a path, read it in order
Path 1 · Start here
Architecture Foundations
Monoliths vs microservices, service boundaries, APIs, consistency, and data modelling.
Path 2 · Intermediate
Scaling & Reliability
From one server to millions of requests: caching, queues, resilience, and high availability.
Path 3 · Intermediate
AI Engineering
RAG pipelines, agents, prompt engineering, model serving, and running LLMs safely in production.
Path 4 · Applied
Shipping to Production
CI/CD, Kubernetes, and observability: the operational side of everything above.
Interactive lab
RAG Pipeline Visualiser
Paste a document and ask a question. Watch in real-time as the RAG pipeline chunks your text, embeds it, retrieves relevant sections, and generates an answer—all animated step-by-step.
- Interactive chunking visualization
- Real-time embedding animation
- Retrieval highlighting
Document
RAG systems combine retrieval and generation to provide grounded answers. The pipeline chunks documents, embeds them, retrieves relevant sections...
Question
How does RAG improve answer accuracy?
4 Chunks
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Who writes this
Ravi Kant Shukla, a senior Java + AI engineer. 9+ years building distributed backends, Kafka pipelines, and production RAG systems. Open to remote senior engineering roles.